Ye N, Wu C, Jiang J
Department of Computer Science and Technology, College of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Sep 20;44(9):1720-1728. doi: 10.12122/j.issn.1673-4254.2024.09.12.
To construct a model with a spatial and channel reconstruction convolutional module for accurate identification and classification of lung sound data.
We propose a convolutional network architecture combining the spatial-channel reconstruction convolution (SCConv) module. A lung sound feature extraction method combining the dual tunable Q-factor wavelet transform (DTQWT) with the triple Wigner-Ville transform (WVT) was used to improve the model's ability to capture the key features of the lung sounds by adaptively focusing on the important channel and spatial features. The performance of the model for classification of normal, crackles, wheezes, and crackles with wheezes was tested using the ICBHI2017 dataset.
The accuracy, sensitivity, specificity and F1 score of the proposed method reached 85.68%, 93.55%, 86.79% and 90.51%, respectively, demonstrating its good performance in classification tasks in the ICBHI2017 lung sound database, especially for distinguishing normal from abnormal lung sounds.
构建一个具有空间和通道重建卷积模块的模型，用于准确识别和分类肺音数据。
我们提出了一种结合空间通道重建卷积（SCConv）模块的卷积网络架构。采用了一种将双可调Q因子小波变换（DTQWT）与三重维格纳-威利变换（WVT）相结合的肺音特征提取方法，通过自适应地关注重要的通道和空间特征，提高模型捕捉肺音关键特征的能力。使用ICBHI2017数据集测试了该模型对正常、湿啰音、哮鸣音以及湿啰音合并哮鸣音的分类性能。
所提方法的准确率、灵敏度、特异性和F1分数分别达到了85.68%、93.55%、86.79%和90.51%，表明其在ICBHI2017肺音数据库的分类任务中表现良好，尤其是在区分正常与异常肺音方面。
